Dynamic Voltage Optimisation of MV/LV Networks

  • Hani Gharavi Ahangar

Student thesis: Doctoral ThesisDoctor of Philosophy


Meeting UK’s 2050 decarbonisation target will require a vast reduction in the carbon footprint of heat, electricity and transport. The Department of Energy and Climate Change in the UK forecast an increase in total electricity demand of more than 60% by 2050. The huge interest in utilizing new low carbon technologies in distribution networks creates major challenges for distribution network operators (DNOs), including voltage and thermal issues. DNOs are increasingly turning to active network management, including Conservation Voltage Reduction (CVR) to address these challenges, as conventional reinforcement is no longer appropriate option due to the high cost and associated disruption.

Conservation Voltage Reduction (CVR) is one of the most effective approaches to dealing with the aforementioned challenges at the distribution level. This thesis explores the potential for implementing dynamic voltage optimisation through CVR on distribution networks in the UK in the presence of high LCT uptake. Studies are conducted for a sample operating year using realistically modelled rural, urban and dense urban distribution networks, representative of networks in the greater Manchester area, which were modelled as part of the “Smart Street ” project. Smart Street was a collaborative project between Electricity North West Limited (ENWL), University of Manchester and Queen’s University Belfast funded by Ofgem’s low carbon fund trialling active network management on 6 MV and 38 LV networks in the greater Manchester area for a period of two years from 2016 to 2018.

Using a modified discrete stochastic optimisation technique called warm-start Oriented Discrete Coordinated Descent (ODCD) to dynamically implement CVR using on load tap changer (OLTC) transformers and switchable capacitors as the control devices, the performance of CVR with regard to reducing energy consumption was investigated on several realistically modelled LV networks for different seasons of a sample year in the UK. The results showed clear benefit of deploying CVR with an average reduction of 8% in energy consumed observed across the different networks and seasons considered.

The potential trade-off that exists between the twin objectives of energy loss on the MV distribution network and energy consumption on the LV side of the network when simultaneously optimising voltage control devises on both networks was studied. A discretised implementation of the Pareto Particle Swarm Optimisation (PPSO) technique was developed to perform the multi-objective optimisation. The technique was successfully benchmarked against an exhaustive search of one of the MV/LV networks exploring all possible feasible solutions. The Pareto Front generated by PPSO indicated that a trade-off does exist between the objectives in question for all the different types of MV/LV network and seasons of the year. However, the trade-off varies depending on type of the network, seasonal and daily load variance and flexibility of the control settings. In particular, the LV energy consumption – MV energy loss trade-off falls off with increasing network flexibility, with the availability of controllable LV transformers being the decisive factor in delivering effective CVR and reduced trade-off.

Coordinated CVR and Electric Vehicle Demand Control (EVDC) via PSO optimisation with a relaxation equality constraint method was investigated on several LV distribution networks considering various scenarios for EV home charging scheduling. Using publicly available statistics for journey length and time of travel of car drivers in the UK, a stochastic model was developed for EV battery State-of-Charge (SOC) and availability for home charging and used to generate the EV charging demand profiles for different levels of EV penetration. The results concluded that a coordinated CVR-EVDC approach enables significantly greater energy savings compared to independent operation of CVR and EVDC – 4% greater with dumb charging and 3% greater when employing valley-filling smart charging. Finally, the impact of several future scenarios with regard to PV and EV load growth to years 2035 and 2050 were investigated for the representative rural, urban and denseurban networks. This investigation showed that with dynamic voltage management of MV/LV networks using the existing network infrastructure, DNOs should be able to accommodate the projected increase in demand on their networks without the need to invest in substantial and costly network reinforcement.
Date of AwardJul 2020
Original languageEnglish
Awarding Institution
  • Queen's University Belfast
SponsorsCarbon Networks Fund (LCNF), Ofgem
SupervisorXueqin Amy Liu (Supervisor) & Seán McLoone (Supervisor)


  • Conservation Voltage Reduction
  • Dynamic Voltage Multi-objective Optimisation
  • MV/LV Loss and Demand Trade-off
  • Electric Vehicle Demand Control

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